Data
acc1_cancer_cells = readRDS("./Data/acc1_cancer_cells_15KnCount_V3.RDS")
all_acc_cancer_cells = readRDS("./Data/acc_cancer_cells_V3.RDS")
acc_all_cells = readRDS("./Data/acc_tpm_nCount_mito_no146_15k_with_ACC1_.RDS")
luminal_pathways = c("CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_DN","CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_UP","CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_DN","CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_UP","HUPER_BREAST_BASAL_VS_LUMINAL_DN","LIM_MAMMARY_LUMINAL_PROGENITOR_UP","SMID_BREAST_CANCER_LUMINAL_B_UP" )
# add luminal pathways
luminal_gs = msigdbr(species = "Homo sapiens") %>%as.data.frame() %>% dplyr::filter(gs_name %in% luminal_pathways)%>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()
Parameters
suffix = "01_25"
data_to_read = ""
functions
source_from_github(repositoy = "DEG_functions",version = "0.2.24")
ℹ SHA-1 hash of file is a183df1c565702ecd8ed338bb2abfb0e13415d8e
source_from_github(repositoy = "HMSC_functions",version = "0.1.12",script_name = "functions.R")
ℹ SHA-1 hash of file is 2934dee5f6b9fee69635192f19b1bcc205e05b62
source_from_github(repositoy = "cNMF_functions",version = "0.3.53",script_name = "cnmf_function_Harmony.R")
ℹ SHA-1 hash of file is a6f56985fe95e4026c7830d7100dd81dd950df3b
UMAP
DimPlot(object = acc1_cancer_cells,pt.size = 2)

features UMAP
FeaturePlot(object = acc1_cancer_cells,features = c("TP63","ACTA2","IL12B","CNN1"))
Warning in FeaturePlot(object = acc1_cancer_cells, features = c("TP63", :
All cells have the same value (0) of IL12B.

Enrichment analysis HMSC vs ACC
patient.ident = all_acc_cancer_cells$patient.ident %>% as.data.frame()
patient.ident[,1] = as.character(patient.ident[,1])
patient.ident[patient.ident[,1] == "ACC1",] = "HMSC"
patient.ident[,1] = as.factor(patient.ident[,1])
all_acc_cancer_cells = AddMetaData(object = all_acc_cancer_cells,metadata = patient.ident,col.name = "patient.ident")
all_acc_cancer_cells = SetIdent(all_acc_cancer_cells, value ="patient.ident")
acc_deg <- FindMarkers(all_acc_cancer_cells, ident.1 = "HMSC",logfc.threshold = 1.5,features = VariableFeatures(all_acc_cancer_cells))
enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells),fdr_Cutoff = 0.01,ident.1 = "HMSC",ident.2 = "ACC",show_by = 1)

cell cycle filtering
hallmark_name = "GO_MITOTIC_CELL_CYCLE"
genesets =getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")
var_features=all_acc_cancer_cells@assays$RNA@var.features
geneIds= genesets[[hallmark_name]]@geneIds
score <- apply(all_acc_cancer_cells@assays$RNA@scale.data[intersect(geneIds,var_features),],2,mean)
all_acc_cancer_cells=AddMetaData(all_acc_cancer_cells,score,hallmark_name)
#filter:
all_acc_cancer_cells_ccFiltered=all_acc_cancer_cells[,all_acc_cancer_cells@meta.data[[hallmark_name]]< 0.3]
min_threshold = min(all_acc_cancer_cells$GO_MITOTIC_CELL_CYCLE)
max_threshold = max(all_acc_cancer_cells$GO_MITOTIC_CELL_CYCLE)
Before cc filtering
FeaturePlot(object = all_acc_cancer_cells,features = hallmark_name) + ggtitle("Before cc filtering") & scale_color_gradientn(colours = plasma(n = 10, direction = -1), limits = c(min_threshold, max_threshold))
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

After cc filtering
FeaturePlot(object = all_acc_cancer_cells_ccFiltered,features = hallmark_name) + ggtitle("After cc filtering") & scale_color_gradientn(colours = plasma(n = 10, direction = -1), limits = c(min_threshold, max_threshold))
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

Enrichment analysis filtered HMSC vs ACC
patient.ident = all_acc_cancer_cells_ccFiltered$patient.ident %>% as.data.frame()
patient.ident[,1] = as.character(patient.ident[,1])
patient.ident[patient.ident[,1] == "ACC1",] = "HMSC"
patient.ident[,1] = as.factor(patient.ident[,1])
all_acc_cancer_cells_ccFiltered = AddMetaData(object = all_acc_cancer_cells_ccFiltered,metadata = patient.ident,col.name = "patient.ident")
all_acc_cancer_cells_ccFiltered = SetIdent(all_acc_cancer_cells_ccFiltered, value ="patient.ident")
acc_deg <- FindMarkers(all_acc_cancer_cells_ccFiltered, ident.1 = "HMSC",logfc.threshold = 1.5,features = VariableFeatures(all_acc_cancer_cells_ccFiltered))
enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells_ccFiltered),fdr_Cutoff = 0.01,ident.1 = "HMSC",ident.2 = "ACC",show_by = 1)

MYB expression
all_acc_cancer_cells = SetIdent(object = all_acc_cancer_cells,value = "patient.ident") #active snn graph
FeaturePlot(object = all_acc_cancer_cells,features = "MYB",label = T)

CNV
new.cluster.ids <- c("cancer", #0
"cancer", #1
"CAF", #2
"cancer", #3
"Endothelial", #4
"cancer", #5
"cancer", #6
"CAF", #7
"CAF", #8
"CAF", #9
"cancer", #10
"CAF", #11
"cancer", #12
"cancer", #13
"cancer", #14
"cancer", #15
"cancer", #16
"WBC", #17
"CAF" #18
)
#rename idents:
acc_all_cells = SetIdent(object = acc_all_cells,value = "RNA_snn_res.1") #active snn graph
names(new.cluster.ids) <- levels(acc_all_cells) #add snn graph levels to new.cluster.ids
acc_all_cells@meta.data[["seurat_clusters"]] = acc_all_cells@meta.data[["RNA_snn_res.1"]]
acc_all_cells = SetIdent(object = acc_all_cells,value = "seurat_clusters")
acc_all_cells <- RenameIdents(acc_all_cells, new.cluster.ids)
# divide "cancer" into patients:
cell_types = acc_all_cells@active.ident %>% as.data.frame()
cell_types[,1]<- as.character(cell_types[,1])
cell_types = cbind(cell_types,acc_all_cells$patient.ident) %>% setnames(old = names(.),
new = c('cell_type','patient'))
cell_types[cell_types$cell_type == "cancer",] = cell_types[cell_types$cell_type == "cancer",2]
# hmsc_rows = (startsWith(x = rownames(cell_types),prefix = "ACC.plate2") | startsWith(x = rownames(cell_types),prefix = "ACC1.")) & cell_types[,1] == "cancer"
# acc_rows = !(startsWith(x = rownames(cell_types),prefix = "ACC.plate2") | startsWith(x = rownames(cell_types),prefix = "ACC1.")) & cell_types[,1] == "cancer"
# cell_types[,1][hmsc_rows] = "HMSC"
# cell_types[,1][acc_rows] = "ACC"
#add to metadata:
cell_types[,2] = NULL
cell_types[cell_types$cell_type == "ACC1",] = "HMSC"
acc_all_cells = AddMetaData(object =acc_all_cells ,metadata = cell_types,col.name = "cell.type")
CNV UMAP

CNV plot
## {-}
CNV subtypes
cnv_subtypes = as.data.frame(cutree(infercnv_obj_default@tumor_subclusters[["hc"]][["HMSC"]], k = 2))
names(cnv_subtypes)[1] = "cnv.cluster"
rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "-",replacement = "\\.")
infercnv.observations = data.frame(fread(file = "./Data/inferCNV/infercnv.observations.txt"), row.names=1)
Warning in fread(file = "./Data/inferCNV/infercnv.observations.txt") :
Detected 1332 column names but the data has 1333 columns (i.e. invalid file). Added 1 extra default column name for the first column which is guessed to be row names or an index. Use setnames() afterwards if this guess is not correct, or fix the file write command that created the file to create a valid file.
names_to_keep = colnames(infercnv.observations) %in% (colnames(acc1_cancer_cells) %>% gsub(pattern = "_",replacement = "\\."))
infercnv.observations = infercnv.observations[,names_to_keep]
rotate <- function(x) t(apply(x, 2, rev))
infercnv.observations2 = infercnv.observations %>% rotate() %>% rotate() %>% rotate()%>% as.data.frame()
breaks = c(0.700891861704857,
0.742366945528369,
0.783842029351881,
0.825317113175393,
0.866792196998905,
0.908267280822417,
0.949742364645928,
0.99121744846944,
1.03269253229295,
1.07416761611646,
1.11564269993998,
1.15711778376349,
1.198592867587,
1.24006795141051,
1.28154303523402,
1.32301811905753)
pheatmap(infercnv.observations2,cluster_cols = F,cluster_rows = F, show_rownames = F,show_colnames = F, breaks = breaks,color = colorRampPalette(rev(c("darkred", "white", "darkblue")))(15),annotation_row = cnv_subtypes)

rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "2\\.",replacement = "2_")
rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "3\\.",replacement = "3_")
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = cnv_subtypes)
DimPlot(acc1_cancer_cells,group.by = "cnv.cluster",pt.size = 2,cols =colors)

Original score
original_myo_genes = c( "TP63", "TP73", "CAV1", "CDH3", "KRT5", "KRT14", "ACTA2", "TAGLN", "MYLK", "DKK3")
original_lum_genes = c("KIT", "EHF", "ELF5", "KRT7", "CLDN3", "CLDN4", "CD24", "LGALS3", "LCN2", "SLPI" )
calculate_score(dataset = all_acc_cancer_cells,myo_genes = original_myo_genes,lum_genes = original_lum_genes)
correlation of lum score and myo score: -0.45
correlation of lum score and original lum score: 1
correlation of myo score and original myo score: 1



Original score of ACC1
calculate_score(dataset = acc1_cancer_cells,myo_genes = original_myo_genes,lum_genes = original_lum_genes,lum_threshold = 0,myo_threshold = 0)
correlation of lum score and myo score: 0.06
correlation of lum score and original lum score: 1
correlation of myo score and original myo score: 1



0.35 Most correlated score
Myo genes
myo_protein_markers = c("CNN1", "TP63","ACTA2")
top_myo = top_correlated(dataset = acc1_cancer_cells, genes = myo_protein_markers,threshold = 0.35)
print("Number of genes = " %>% paste(length(top_myo)))
[1] "Number of genes = 20"
message("Names of genes:")
Names of genes:
top_myo %>% head(30)
[1] "COL16A1" "RP1-39G22.4" "ACTG2" "CD200" "MYLK" "TP63" "KCNMB1"
[8] "ADAMTS2" "LOXL2" "TPM2" "CLIC3" "SNCG" "ACTA2" "TAGLN"
[15] "A2M" "NGFR" "CNN1" "PPP1R14A" "MYL9" "POM121L9P"
message("Genes that also apeared in the original score:")
Genes that also apeared in the original score:
base::intersect(top_myo,original_myo_genes)
[1] "MYLK" "TP63" "ACTA2" "TAGLN"
myo_enrich_res = genes_vec_enrichment(genes = top_myo,background = rownames(acc1_cancer_cells),homer = T,title = "myo top enrichment",custom_pathways = luminal_gs)

myo_enrich_res
Lum genes
lum_protein_markers = c("KIT")
top_lum = top_correlated(dataset = acc1_cancer_cells, genes = lum_protein_markers,threshold = 0.35,n_vargenes = 5000)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
print("Number of genes = " %>% paste(length(top_lum)))
[1] "Number of genes = 5"
message("Names of genes:")
Names of genes:
top_lum %>% head(30)
[1] "B3GNT2" "GLRB" "EFNA5" "ALDH3B2" "CCND1"
message("Genes that also apeared in the original score:")
Genes that also apeared in the original score:
base::intersect(top_lum,original_lum_genes)
character(0)
lum_enrich_res = genes_vec_enrichment(genes = top_lum,background = rownames(acc1_cancer_cells),homer = T,title = "lum top enrichment",custom_pathways = luminal_gs)

lum_enrich_res
enriched genes score
rownames(lum_enrich_res) = lum_enrich_res$pathway_name
lum_enriched_genes = lum_enrich_res[1,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,lum_protein_markers) #add original markers
rownames(myo_enrich_res) = myo_enrich_res$pathway_name
myo_enriched_genes = myo_enrich_res[1,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,myo_protein_markers) #add original markers
calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = -2.5,myo_threshold = -2.5)
correlation of lum score and myo score: -0.16
correlation of lum score and original lum score: 0.43
correlation of myo score and original myo score: 0.79



0.2 Most correlated score
myo Genes
n_vargenes = 2000
myo_protein_markers = c("CNN1", "TP63","ACTA2")
top_myo = top_correlated(dataset = acc1_cancer_cells, genes = myo_protein_markers,threshold = 0.2,n_vargenes = n_vargenes)
print("Number of genes = " %>% paste(length(top_myo)))
[1] "Number of genes = 48"
message("Names of genes:")
Names of genes:
top_myo %>% head(30)
[1] "PLOD1" "RP1-39G22.4" "PDZK1" "CSRP1" "CHI3L1" "HIST3H3"
[7] "AC104699.1" "ACTG2" "LYG1" "DALRD3" "CD200" "RP11-627C21.1"
[13] "FGFBP2" "IGFBP7" "HSPB3" "RP11-168A11.4" "SPARC" "CD83"
[19] "HLA-DOB" "TBCC" "NFKBIE" "MIR3662" "SMOC2" "FBXL6"
[25] "TPM2" "SNCG" "ACTA2" "DKK3" "MIR7113" "CTA-797E19.3"
message("Genes that also apeared in the original score:")
Genes that also apeared in the original score:
base::intersect(top_myo,original_myo_genes)
[1] "ACTA2" "DKK3" "TAGLN"
myo_enrich_res = genes_vec_enrichment(genes = top_myo,background = VariableFeatures(acc1_cancer_cells) %>% head(n_vargenes),homer = T,title = "myo top enrichment",custom_pathways = luminal_gs)

myo_enrich_res
Lum Genes
acc1_cancer_cells = FindVariableFeatures(object = acc1_cancer_cells,nfeatures = 15000)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
lum_protein_markers = c("KIT")
n_vargenes = 2000
top_lum = top_correlated(dataset = acc1_cancer_cells, genes = lum_protein_markers,threshold = 0.3,n_vargenes = n_vargenes)
print("Number of genes = " %>% paste(length(top_lum)))
[1] "Number of genes = 13"
message("Names of genes:")
Names of genes:
top_lum %>% head(30)
[1] "CSDE1" "RRP9" "TKT" "TFG" "ATP1B3" "EFNA5" "GABRP" "CALML5" "MMP7" "SNORD68"
[11] "APP" "SDF2L1" "SAT1"
message("Genes that also apeared in the original score:")
Genes that also apeared in the original score:
base::intersect(top_lum,original_lum_genes)
character(0)
lum_enrich_res = genes_vec_enrichment(genes = top_lum,background = VariableFeatures(acc1_cancer_cells) %>% head(n_vargenes),homer = T,title = "lum top enrichment",custom_pathways = luminal_gs)

lum_enrich_res
calculate_score(dataset = acc1_cancer_cells,myo_genes = top_myo,lum_genes = top_lum,lum_threshold = 2,myo_threshold = 1)
correlation of lum score and myo score: -0.02
correlation of lum score and original lum score: 0.65
correlation of myo score and original myo score: 0.77



myo_intersected = intersect(top_myo,original_myo_genes)
lum_intersected = intersect(top_lum,original_lum_genes)
message("genes in myo score:")
genes in myo score:
myo_intersected
[1] "ACTA2" "DKK3" "TAGLN"
message("genes in lum score:")
genes in lum score:
lum_intersected
[1] "LGALS3"
calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_intersected,lum_genes = lum_intersected,lum_threshold = 2,myo_threshold = 1)
correlation of lum score and myo score: 0.03
correlation of lum score and original lum score: 0.69
correlation of myo score and original myo score: 0.81



enriched genes
rownames(lum_enrich_res) = lum_enrich_res$pathway_name
lum_enriched_genes = lum_enrich_res[3,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,lum_protein_markers) #add original markers
rownames(myo_enrich_res) = myo_enrich_res$pathway_name
myo_enriched_genes = myo_enrich_res[3,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,myo_protein_markers) #add original markers
message("genes in myo score:")
genes in myo score:
myo_enriched_genes
[1] "FBLIM1" "NEXN" "NMNAT2" "MYLK" "CCDC50" "IGFBP7" "RAI14" "ARAP3" "SPARC" "CALD1" "LOXL2"
[12] "COL5A1" "ACTA2" "DKK3" "MSRB3" "COL4A1" "ACTN1" "TPM1" "TGFB1I1" "ADORA2B" "MXRA7" "CNN1"
[23] "TP63" "ACTA2"
message("genes in lum score:")
genes in lum score:
lum_enriched_genes
[1] "PADI2" "PATJ" "PEX11B" "APH1A" "C1orf43" "EFNA4" "NECTIN4" "SCYL3" "ELF3"
[10] "SOX13" "IRF6" "MED28" "EPB41L4A" "RGL2" "C6orf132" "TPD52L1" "ICA1" "MACC1"
[19] "TRPS1" "FAM83H" "RASEF" "ARRDC1" "COMMD3" "ANK3" "GSTO2" "PDCD4" "EHF"
[28] "ALDH3B2" "SHANK2" "SORL1" "FKBP4" "PTPN6" "DUSP16" "RERG" "ADCY6" "ERBB3"
[37] "ERP29" "SUSD6" "RPS6KA5" "SPINT1" "FEM1B" "TLE3" "SCAMP2" "CLN3" "ADGRG1"
[46] "ATP2C2" "GGT6" "MYO1D" "ST6GALNAC2" "CYB5A" "BLVRB" "VRK3" "SYCP2" "TMPRSS2"
[55] "LIMK2" "KIT"
calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = 0,myo_threshold = -1)
correlation of lum score and myo score: 0.1
correlation of lum score and original lum score: 0.71
correlation of myo score and original myo score: 0.76



enriched genes and in original score
myo_enriched_genes = myo_enriched_genes[myo_enriched_genes %in% original_myo_genes]
lum_enriched_genes = lum_enriched_genes[lum_enriched_genes %in% original_lum_genes]
message("genes in myo score:")
genes in myo score:
myo_enriched_genes
[1] "MYLK" "ACTA2" "DKK3" "TP63" "ACTA2"
message("genes in lum score:")
genes in lum score:
lum_enriched_genes
[1] "EHF" "KIT"
calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = 2,myo_threshold = 2)
correlation of lum score and myo score: -0.07
correlation of lum score and original lum score: 0.62
correlation of myo score and original myo score: 0.77



HPV
Only HMSC cancer cells:
HPV33_P3 = fread("./Data/HPV33_P3.txt",col.names = c("plate","reads")) %>% as.data.frame()
HPV33_P3.df = HPV33_P3 %>% mutate(
plate = gsub(x =HPV33_P3$plate, replacement = "",pattern = "_.*$")
%>% gsub(pattern = "-P",replacement = ".P")
%>% gsub(pattern = "-",replacement = "_",)
)
HPV33_P3.df = HPV33_P3.df %>% dplyr::filter(HPV33_P3.df$plate %in% colnames(acc1_cancer_cells))
rownames(HPV33_P3.df) <- HPV33_P3.df$plate
HPV33_P3.df$plate = NULL
HPV33_P2 = fread("./Data/HPV33_P2.txt",col.names = c("plate","reads")) %>% as.data.frame()
HPV33_P2.df = HPV33_P2 %>% mutate(
plate = gsub(x =HPV33_P2$plate, replacement = "",pattern = "_.*$")
%>% gsub(pattern = "plate2-",replacement = "plate2_",)
%>% gsub(pattern = "-",replacement = "\\.",)
)
HPV33_P2.df = HPV33_P2.df %>% dplyr::filter(HPV33_P2.df$plate %in% colnames(acc1_cancer_cells))
rownames(HPV33_P2.df) <- HPV33_P2.df$plate
HPV33_P2.df$plate = NULL
HPV33 = rbind(HPV33_P3.df,HPV33_P2.df)
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = HPV33,col.name = "HPV33.reads")
FeaturePlot(acc1_cancer_cells,features = "HPV33.reads",max.cutoff = 40)

data = FetchData(object = acc1_cancer_cells,vars = "HPV33.reads")
print(
data %>%
ggplot(aes( x=HPV33.reads)) +
geom_density()
)

hpv33_positive = HPV33 %>% dplyr::mutate(hpv33_positive = case_when(reads >= 750 ~"strong positive",
reads < 750 & reads > 10 ~ "positive",
reads < 10 ~ "negative")
)
hpv33_positive$reads = NULL
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = hpv33_positive)
DimPlot(object = acc1_cancer_cells,group.by = c("hpv33_positive"),pt.size = 2)

cNMF
library(reticulate)
#write expression
acc1_cancer_cells = FindVariableFeatures(object = acc1_cancer_cells,nfeatures = 2000)
vargenes = VariableFeatures(object = acc1_cancer_cells)
hmsc_expression = t(as.matrix(GetAssayData(acc1_cancer_cells,slot='data')))
hmsc_expression = 2**hmsc_expression #convert from log2(tpm+1) to tpm
hmsc_expression = hmsc_expression-1
# hmsc_expression = hmsc_expression[,!colSums(hmsc_expression==0, na.rm=TRUE)==nrow(hmsc_expression)] #delete rows that have all 0
hmsc_expression = hmsc_expression[,vargenes]
write.table(x = hmsc_expression ,file = './Data/cNMF/hmsc_expressionData_2Kvargenes.txt',sep = "\t")
from cnmf import cNMF
name = 'HMSC_cNMF_2Kvargenes'
outdir = './Data/cNMF'
K_range = np.arange(3,10)
cnmf_obj = cNMF(output_dir=outdir, name=name)
counts_fn='./Data/cNMF/hmsc_expressionData_2Kvargenes.txt'
tpm_fn = counts_fn ## This is a weird case where because this dataset is not 3' end umi sequencing, we opted to use the TPM matrix as the input matrix rather than the count matrix
cnmf_obj.prepare(counts_fn=counts_fn, components=K_range, seed=14,tpm_fn=tpm_fn)
cnmf_obj.factorize(worker_i=0, total_workers=1)
cnmf_obj.combine()
cnmf_obj.k_selection_plot()
Save object
import pickle
f = open('./Data/cNMF/HMSC_cNMF_2Kvargenes/cnmf_obj.pckl', 'wb')
pickle.dump(cnmf_obj, f)
f.close()
Load object
from cnmf import cNMF
import pickle
f = open('./Data/cNMF/HMSC_cNMF_2Kvargenes/cnmf_obj.pckl', 'rb')
cnmf_obj = pickle.load(f)
f.close()
selected_k = 4
density_threshold = 0.1
cnmf_obj.consensus(k=selected_k, density_threshold=density_threshold,show_clustering=True)
/sci/labs/yotamd/lab_share/avishai.wizel/python_envs/miniconda/envs/cnmf_env_6/lib/python3.7/site-packages/scanpy/preprocessing/_simple.py:843: UserWarning: Received a view of an AnnData. Making a copy.
view_to_actual(adata)
usage_norm, gep_scores, gep_tpm, topgenes = cnmf_obj.load_results(K=selected_k, density_threshold=density_threshold)
# calculate usage by expression*genes coefs:
gep_scores = py$gep_scores
all_metagenes= py$usage_norm
Enrichment analysis by top 200 genes of each program
plt_list = list()
for (i in 1:ncol(gep_scores)) {
top_genes = gep_scores %>% arrange(desc(gep_scores[i])) #sort by score a
top = head(rownames(top_genes),200) #take top top_genes_num
res = genes_vec_enrichment(genes = top,background = rownames(gep_scores),homer = T,title =
i,silent = T,return_all = T)
plt_list[[i]] = res$plt
}
gridExtra::grid.arrange(grobs = plt_list)

plt_list = list()
for (i in 1:ncol(gep_scores)) {
top_genes = gep_scores %>% arrange(desc(gep_scores[i])) #sort by score a
top = head(rownames(top_genes),10) #take top top_genes_num
message(paste("program ",i,"top genes:"))
print(top)
}
program 1 top genes:
[1] "IGKV5-2" "NDUFB7" "LGALS1" "UBL5" "S100A6" "S100A11" "CUTA" "IFITM3" "JTB" "IL17RB"
program 2 top genes:
[1] "EGR1" "C6orf62" "JUNB" "DNAJA1" "ATF3" "IER2" "ERRFI1" "KLF6" "CDKN1A" "MTHFD2"
program 3 top genes:
[1] "RP1-128M12.3" "RP11-374F3.3" "RP11-403A21.3" "RP11-454L9.2" "AC097500.1" "RP11-463H24.4"
[7] "RP11-515O17.2" "RP11-536L3.4" "PALD1" "AL049758.2"
program 4 top genes:
[1] "LTF" "PIGR" "FMO2" "HSD17B2" "MLPH" "PRR15L"
[7] "RF00019.219" "RP11-817J15.2" "CD14" "CLU"
# Make metagene names
for (i in 1:ncol(all_metagenes)) {
colnames(all_metagenes)[i] = "metagene." %>% paste0(i)
}
#add each metagene to metadata
for (i in 1:ncol(all_metagenes)) {
metage_metadata = all_metagenes %>% select(i)
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = metage_metadata)
}
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(i)` instead of `i` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
FeaturePlot(object = acc1_cancer_cells,features = colnames(all_metagenes))

assignment UMAP
larger_by = 2
message(paste("larger_by = ", larger_by))
larger_by = 2
acc1_cancer_cells = program_assignment(dataset = acc1_cancer_cells,larger_by = larger_by,program_names = colnames(all_metagenes))
selected_k =4
colors = rainbow(selected_k)
colors = c(colors,"grey")
DimPlot(acc1_cancer_cells,group.by = "program.assignment",pt.size = 2,cols =colors)

show cell cycle program:
hallmark_name = "GO_MITOTIC_CELL_CYCLE"
genesets =GSEABase::getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")
var_features=acc1_cancer_cells@assays$RNA@var.features
geneIds= genesets[[hallmark_name]]@geneIds
score <- apply(acc1_cancer_cells@assays$RNA@data[intersect(geneIds,var_features),],2,mean)
acc1_cancer_cells=AddMetaData(acc1_cancer_cells,score,hallmark_name)
FeaturePlot(object = acc1_cancer_cells,features = hallmark_name)

cc_vs_program2 = FetchData(object = acc1_cancer_cells,vars = c("metagene.2",hallmark_name))
cor(cc_vs_program2[1],cc_vs_program2[2])
GO_MITOTIC_CELL_CYCLE
metagene.2 0.611673
Comparisions
cnv_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","hpv33_positive"))
test <- fisher.test(table(cnv_vs_hpv))
ggbarstats(
cnv_vs_hpv, cnv.cluster, hpv33_positive,
results.subtitle = FALSE,
subtitle = paste0(
"Fisher's exact test", ", p-value = ",
ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
)
)

cnv_vs_cnmf = FetchData(object = acc1_cancer_cells,vars = c("program.assignment","cnv.cluster"))
cnv_vs_cnmf = cnv_vs_cnmf %>% dplyr::filter(program.assignment == "1" |program.assignment == "2" )
test <- fisher.test(table(cnv_vs_cnmf))
ggbarstats(
cnv_vs_cnmf, program.assignment, cnv.cluster,
results.subtitle = FALSE,
subtitle = paste0(
"Fisher's exact test", ", p-value = ",
ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
)
)

cnmf_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("program.assignment","hpv33_positive"))
cnmf_vs_hpv = cnmf_vs_hpv %>% dplyr::filter(program.assignment == "1" |program.assignment == "2" )
test <- fisher.test(table(cnmf_vs_hpv))
ggbarstats(
cnmf_vs_hpv, program.assignment, hpv33_positive,
results.subtitle = FALSE,
subtitle = paste0(
"Fisher's exact test", ", p-value = ",
ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
)
)

myb_vs_cnv = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","MYB"))
myb_vs_cnv $cnv.cluster = as.character(myb_vs_cnv $cnv.cluster )
ggboxplot(myb_vs_cnv, x = "cnv.cluster", y = "MYB",
palette = "jco",
add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("1","2")))

myb_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("hpv33_positive","MYB"))
myb_vs_hpv $hpv33_positive = as.character(myb_vs_hpv $hpv33_positive )
ggboxplot(myb_vs_hpv, x = "hpv33_positive", y = "MYB",
palette = "jco",
add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("positive","negative"),c("strong positive", "positive"),c("strong positive", "negative")))+ stat_summary(fun.data = function(x) data.frame(y=15, label = paste("Mean=",round(mean(x),digits = 2))), geom="text") +ylab("log2(MYB)")
Warning in wilcox.test.default(c(7.30445531374182, 0, 7.65159269158999, :
cannot compute exact p-value with ties

hpvReads_vs_myb = FetchData(object = acc1_cancer_cells,vars = c("HPV33.reads","MYB"))
corr = cor(hpvReads_vs_myb$HPV33.reads,hpvReads_vs_myb$MYB)
print("correlation of MYB abd hpv33_reads:" %>% paste(corr %>% round(digits = 2)) )
[1] "correlation of MYB abd hpv33_reads: 0.21"
---
title: "Title"
author: "Avishai Wizel"
date: '`r Sys.time()`'
output: 
  html_notebook: 
    code_folding: hide
---
```{r, include=FALSE}
knitr::opts_chunk$set(
  include = T
)
```

## Data

```{r}
acc1_cancer_cells = readRDS("./Data/acc1_cancer_cells_15KnCount_V3.RDS")
all_acc_cancer_cells = readRDS("./Data/acc_cancer_cells_V3.RDS")
acc_all_cells = readRDS("./Data/acc_tpm_nCount_mito_no146_15k_with_ACC1_.RDS")
luminal_pathways = c("CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_DN","CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_UP","CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_DN","CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_UP","HUPER_BREAST_BASAL_VS_LUMINAL_DN","LIM_MAMMARY_LUMINAL_PROGENITOR_UP","SMID_BREAST_CANCER_LUMINAL_B_UP" )

# add luminal pathways
luminal_gs = msigdbr(species = "Homo sapiens") %>%as.data.frame() %>% dplyr::filter(gs_name %in% luminal_pathways)%>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()

```

## Parameters

```{r warning=FALSE}
suffix = "01_25"
data_to_read = ""
```


## functions

```{r warning=FALSE}
source_from_github(repositoy = "DEG_functions",version = "0.2.24")
source_from_github(repositoy = "HMSC_functions",version = "0.1.12",script_name = "functions.R")
source_from_github(repositoy = "cNMF_functions",version = "0.3.53",script_name = "cnmf_function_Harmony.R")
```
## UMAP
```{r }
DimPlot(object = acc1_cancer_cells,pt.size = 2)
```
## features UMAP
```{r fig.width=10}
FeaturePlot(object = acc1_cancer_cells,features = c("TP63","ACTA2","IL12B","CNN1"))
```


## Enrichment analysis HMSC vs ACC
```{r fig.width=8, echo=TRUE,results='hide',fig.keep='all'}
patient.ident = all_acc_cancer_cells$patient.ident %>% as.data.frame()
patient.ident[,1] = as.character(patient.ident[,1])
patient.ident[patient.ident[,1] == "ACC1",] = "HMSC"
patient.ident[,1] = as.factor(patient.ident[,1])
all_acc_cancer_cells = AddMetaData(object = all_acc_cancer_cells,metadata = patient.ident,col.name = "patient.ident")
all_acc_cancer_cells = SetIdent(all_acc_cancer_cells, value ="patient.ident")
acc_deg <- FindMarkers(all_acc_cancer_cells, ident.1 = "HMSC",logfc.threshold = 1.5,features = VariableFeatures(all_acc_cancer_cells))
enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells),fdr_Cutoff = 0.01,ident.1 = "HMSC",ident.2 = "ACC",show_by = 1)
```

## cell cycle filtering {.tabset}


```{r warning=FALSE}
hallmark_name = "GO_MITOTIC_CELL_CYCLE"
genesets  =getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")
var_features=all_acc_cancer_cells@assays$RNA@var.features
geneIds= genesets[[hallmark_name]]@geneIds
score <- apply(all_acc_cancer_cells@assays$RNA@scale.data[intersect(geneIds,var_features),],2,mean)
all_acc_cancer_cells=AddMetaData(all_acc_cancer_cells,score,hallmark_name)

#filter:
all_acc_cancer_cells_ccFiltered=all_acc_cancer_cells[,all_acc_cancer_cells@meta.data[[hallmark_name]]< 0.3]


min_threshold = min(all_acc_cancer_cells$GO_MITOTIC_CELL_CYCLE)
max_threshold = max(all_acc_cancer_cells$GO_MITOTIC_CELL_CYCLE)
```


### Before cc filtering


```{r}
FeaturePlot(object = all_acc_cancer_cells,features = hallmark_name) + ggtitle("Before cc filtering")  & scale_color_gradientn(colours = plasma(n = 10, direction = -1), limits = c(min_threshold, max_threshold))
```

### After cc filtering

```{r}
FeaturePlot(object = all_acc_cancer_cells_ccFiltered,features = hallmark_name) + ggtitle("After cc filtering") & scale_color_gradientn(colours = plasma(n = 10, direction = -1), limits = c(min_threshold, max_threshold))
```
## {-}

## Enrichment analysis filtered HMSC vs ACC
```{r fig.width=8, echo=TRUE,results='hide',fig.keep='all'}
patient.ident = all_acc_cancer_cells_ccFiltered$patient.ident %>% as.data.frame()
patient.ident[,1] = as.character(patient.ident[,1])
patient.ident[patient.ident[,1] == "ACC1",] = "HMSC"
patient.ident[,1] = as.factor(patient.ident[,1])
all_acc_cancer_cells_ccFiltered = AddMetaData(object = all_acc_cancer_cells_ccFiltered,metadata = patient.ident,col.name = "patient.ident")
all_acc_cancer_cells_ccFiltered = SetIdent(all_acc_cancer_cells_ccFiltered, value ="patient.ident")
acc_deg <- FindMarkers(all_acc_cancer_cells_ccFiltered, ident.1 = "HMSC",logfc.threshold = 1.5,features = VariableFeatures(all_acc_cancer_cells_ccFiltered))
enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells_ccFiltered),fdr_Cutoff = 0.01,ident.1 = "HMSC",ident.2 = "ACC",show_by = 1)
```

## MYB expression

```{r fig.width=10}
all_acc_cancer_cells = SetIdent(object = all_acc_cancer_cells,value = "patient.ident") #active snn graph
FeaturePlot(object = all_acc_cancer_cells,features = "MYB",label = T)
```

## CNV {.tabset}



```{r}
#set cell types
new.cluster.ids <- c("cancer", #0
                     "cancer", #1
                     "CAF", #2
                     "cancer", #3
                     "Endothelial", #4
                     "cancer", #5
                     "cancer", #6
                     "CAF", #7
                     "CAF", #8
                     "CAF", #9
                     "cancer", #10
                     "CAF", #11
                     "cancer", #12
                     "cancer", #13
                     "cancer", #14
                     "cancer", #15
                     "cancer", #16
                     "WBC", #17
                     "CAF" #18
                     )
```


```{r fig.show='hide'}
#rename idents:
acc_all_cells = SetIdent(object = acc_all_cells,value = "RNA_snn_res.1") #active snn graph
names(new.cluster.ids) <- levels(acc_all_cells) #add snn graph levels to new.cluster.ids
acc_all_cells@meta.data[["seurat_clusters"]] = acc_all_cells@meta.data[["RNA_snn_res.1"]]
acc_all_cells = SetIdent(object = acc_all_cells,value = "seurat_clusters")
acc_all_cells <- RenameIdents(acc_all_cells, new.cluster.ids) 

# divide "cancer" into patients:
cell_types = acc_all_cells@active.ident %>% as.data.frame()
cell_types[,1]<- as.character(cell_types[,1])
cell_types = cbind(cell_types,acc_all_cells$patient.ident) %>% setnames(old = names(.), 
         new = c('cell_type','patient'))
cell_types[cell_types$cell_type == "cancer",] = cell_types[cell_types$cell_type == "cancer",2]


# hmsc_rows = (startsWith(x = rownames(cell_types),prefix = "ACC.plate2") | startsWith(x = rownames(cell_types),prefix = "ACC1.")) & cell_types[,1] == "cancer" 
# acc_rows = !(startsWith(x = rownames(cell_types),prefix = "ACC.plate2") | startsWith(x = rownames(cell_types),prefix = "ACC1.")) & cell_types[,1] == "cancer" 
# cell_types[,1][hmsc_rows]  = "HMSC"
# cell_types[,1][acc_rows]  = "ACC"

#add to metadata:
cell_types[,2] = NULL 
cell_types[cell_types$cell_type == "ACC1",] = "HMSC"
acc_all_cells = AddMetaData(object =acc_all_cells ,metadata = cell_types,col.name = "cell.type")
```
### CNV UMAP 

```{r fig.width=10}
library(infercnv)
library(tidyverse)
acc_annotation  = as.data.frame(acc_all_cells@meta.data[,"cell.type",drop = F])
acc_annotation = acc_annotation %>% rownames_to_column("orig.ident") 
acc_annotation = acc_annotation %>% mutate(orig.ident = gsub(x = acc_annotation$orig.ident,pattern = "\\.", replacement = "-") %>% 
  gsub(pattern = "_", replacement = "-"))
  

write.table(acc_annotation, "./Data/inferCNV/acc_annotation.txt", append = FALSE, 
            sep = "\t", dec = ".",row.names = FALSE, col.names = F)

infercnv_obj = CreateInfercnvObject(raw_counts_matrix="./Data/inferCNV/all.4icnv.txt", 
                                    annotations_file="./Data/inferCNV/acc_annotation.txt",
                                    delim="\t",gene_order_file="./Data/inferCNV/gencode_v19_gene_pos.txt"
                                    ,ref_group_names=c("CAF", "Endothelial", "WBC")) #groups of normal cells

infercnv_obj_default = infercnv::run(infercnv_obj, cutoff=1, out_dir='./Data/inferCNV/infercnv_output',
                                     cluster_by_groups=T, plot_steps=FALSE,
                                     denoise=TRUE, HMM=FALSE, no_prelim_plot=TRUE,
                                     png_res=300)
plot_cnv(infercnv_obj_default, output_format = "png",  write_expr_matrix = FALSE,out_dir = "./Data/inferCNV/",png_res	=800,obs_title = "Malignant cells",ref_title = "Normal cells")


cluster.info=FetchData(acc_all_cells,c("ident","orig.ident","UMAP_1","UMAP_2","nCount_RNA","nFeature_RNA","percent.mt","patient.ident","seurat_clusters"))
cluster.info$cell=rownames(cluster.info)

library(limma)
smoothed=apply(infercnv_obj_default@expr.data,2,tricubeMovingAverage, span=0.01)
cnsig=sqrt(apply((smoothed-1)^2,2,mean))
umap=cluster.info
names(cnsig)=umap$cell

acc_all_cells <- AddMetaData(object = acc_all_cells, metadata = cnsig, col.name = "copynumber")
acc_all_cells = SetIdent(object = acc_all_cells,value = "cell.type")
FeaturePlot(acc_all_cells, "copynumber",pt.size = 1, cols = c("lightblue","orange","red","darkred"),label = T,repel = T)
```


### CNV plot 

![CNV plot](/sci/labs/yotamd/lab_share/avishai.wizel/R_projects/HMSC/Data/inferCNV/infercnv.png)
## {-}

## CNV subtypes

```{r}
cnv_subtypes = as.data.frame(cutree(infercnv_obj_default@tumor_subclusters[["hc"]][["HMSC"]], k = 2))
names(cnv_subtypes)[1] = "cnv.cluster"
rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "-",replacement = "\\.")
infercnv.observations = data.frame(fread(file = "./Data/inferCNV/infercnv.observations.txt"), row.names=1)
names_to_keep = colnames(infercnv.observations) %in% (colnames(acc1_cancer_cells) %>% gsub(pattern = "_",replacement = "\\."))
infercnv.observations = infercnv.observations[,names_to_keep]
```

```{r}
rotate <- function(x) t(apply(x, 2, rev))
infercnv.observations2 = infercnv.observations %>% rotate() %>%  rotate() %>% rotate()%>% as.data.frame() 
breaks = c(0.700891861704857,
0.742366945528369,
0.783842029351881,
0.825317113175393,
0.866792196998905,
0.908267280822417,
0.949742364645928,
0.99121744846944,
1.03269253229295,
1.07416761611646,
1.11564269993998,
1.15711778376349,
1.198592867587,
1.24006795141051,
1.28154303523402,
1.32301811905753)
pheatmap(infercnv.observations2,cluster_cols = F,cluster_rows = F, show_rownames = F,show_colnames = F, breaks = breaks,color = colorRampPalette(rev(c("darkred", "white", "darkblue")))(15),annotation_row = cnv_subtypes)

```

```{r}
rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "2\\.",replacement = "2_")
rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "3\\.",replacement = "3_")
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = cnv_subtypes)
```

```{r}
DimPlot(acc1_cancer_cells,group.by = "cnv.cluster",pt.size = 2,cols =colors)
```


## Original score
```{r}
original_myo_genes = c( "TP63", "TP73", "CAV1", "CDH3", "KRT5", "KRT14", "ACTA2", "TAGLN", "MYLK", "DKK3")
original_lum_genes = c("KIT", "EHF", "ELF5", "KRT7", "CLDN3", "CLDN4", "CD24", "LGALS3", "LCN2", "SLPI" )
```



```{r}
calculate_score(dataset = all_acc_cancer_cells,myo_genes = original_myo_genes,lum_genes = original_lum_genes)
```
## Original score of ACC1
```{r}
calculate_score(dataset = acc1_cancer_cells,myo_genes = original_myo_genes,lum_genes = original_lum_genes,lum_threshold = 0,myo_threshold = 0)
```


## 0.35 Most correlated score {.tabset}

### Myo genes


```{r warning=FALSE, collapse=T}
myo_protein_markers = c("CNN1", "TP63","ACTA2")
top_myo  = top_correlated(dataset = acc1_cancer_cells, genes = myo_protein_markers,threshold = 0.35)
print("Number of genes = " %>% paste(length(top_myo)))
message("Names of genes:")
top_myo %>% head(30)
message("Genes that also apeared in the original score:")
base::intersect(top_myo,original_myo_genes) 
```
```{r}
myo_enrich_res = genes_vec_enrichment(genes = top_myo,background = rownames(acc1_cancer_cells),homer = T,title = "myo top enrichment",custom_pathways = luminal_gs)
myo_enrich_res
```
### Lum genes
```{r}
lum_protein_markers = c("KIT")
top_lum  = top_correlated(dataset = acc1_cancer_cells, genes = lum_protein_markers,threshold = 0.35,n_vargenes = 5000)
print("Number of genes = " %>% paste(length(top_lum)))
message("Names of genes:")
top_lum %>% head(30)
message("Genes that also apeared in the original score:")
base::intersect(top_lum,original_lum_genes) 
```

```{r}
lum_enrich_res = genes_vec_enrichment(genes = top_lum,background = rownames(acc1_cancer_cells),homer = T,title = "lum top enrichment",custom_pathways = luminal_gs)
lum_enrich_res
```
### top correlated score
```{r}
calculate_score(dataset = acc1_cancer_cells,myo_genes = top_myo,lum_genes = top_lum,lum_threshold = 0,myo_threshold = 0)
```


###  enriched genes score
```{r}
rownames(lum_enrich_res) = lum_enrich_res$pathway_name
lum_enriched_genes = lum_enrich_res[1,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,lum_protein_markers) #add original markers
```

```{r}
rownames(myo_enrich_res) = myo_enrich_res$pathway_name
myo_enriched_genes = myo_enrich_res[1,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,myo_protein_markers) #add original markers
```

```{r}
calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = -2.5,myo_threshold = -2.5)
```


## {-}


##  0.2 Most correlated score {.tabset}

###  myo Genes

```{r}

```

```{r warning=FALSE}
n_vargenes = 2000
myo_protein_markers = c("CNN1", "TP63","ACTA2")
top_myo  = top_correlated(dataset = acc1_cancer_cells, genes = myo_protein_markers,threshold = 0.2,n_vargenes = n_vargenes)
print("Number of genes = " %>% paste(length(top_myo)))
message("Names of genes:")
top_myo %>% head(30)
message("Genes that also apeared in the original score:")
base::intersect(top_myo,original_myo_genes) 
myo_enrich_res = genes_vec_enrichment(genes = top_myo,background = VariableFeatures(acc1_cancer_cells) %>% head(n_vargenes),homer = T,title = "myo top enrichment",custom_pathways = luminal_gs)
myo_enrich_res
```

###  Lum Genes

```{r}
acc1_cancer_cells = FindVariableFeatures(object = acc1_cancer_cells,nfeatures = 15000)
```

```{r}
lum_protein_markers = c("KIT")
n_vargenes = 2000
top_lum  = top_correlated(dataset = acc1_cancer_cells, genes = lum_protein_markers,threshold = 0.3,n_vargenes = n_vargenes)
print("Number of genes = " %>% paste(length(top_lum)))
message("Names of genes:")
top_lum %>% head(30)
message("Genes that also apeared in the original score:")
base::intersect(top_lum,original_lum_genes) 

lum_enrich_res = genes_vec_enrichment(genes = top_lum,background = VariableFeatures(acc1_cancer_cells) %>% head(n_vargenes),homer = T,title = "lum top enrichment",custom_pathways = luminal_gs)
lum_enrich_res
calculate_score(dataset = acc1_cancer_cells,myo_genes = top_myo,lum_genes = top_lum,lum_threshold = 2,myo_threshold = 1)
```


```{r}
myo_intersected = intersect(top_myo,original_myo_genes) 
lum_intersected = intersect(top_lum,original_lum_genes) 
message("genes in myo score:")
myo_intersected

message("genes in lum score:")
lum_intersected
calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_intersected,lum_genes = lum_intersected,lum_threshold = 2,myo_threshold = 1)
```



### enriched genes
```{r}
rownames(lum_enrich_res) = lum_enrich_res$pathway_name
lum_enriched_genes = lum_enrich_res[3,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,lum_protein_markers) #add original markers
```

```{r}
rownames(myo_enrich_res) = myo_enrich_res$pathway_name
myo_enriched_genes = myo_enrich_res[3,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,myo_protein_markers) #add original markers
```

```{r}
message("genes in myo score:")
myo_enriched_genes

message("genes in lum score:")
lum_enriched_genes

calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = 0,myo_threshold = -1)
```


### enriched genes and in original score
```{r}
myo_enriched_genes = myo_enriched_genes[myo_enriched_genes %in% original_myo_genes]
lum_enriched_genes = lum_enriched_genes[lum_enriched_genes %in% original_lum_genes]

message("genes in myo score:")
myo_enriched_genes

message("genes in lum score:")
lum_enriched_genes

calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = 2,myo_threshold = 2)
```

## {-}


# HPV

Only HMSC cancer cells:

```{r}
HPV33_P3 = fread("./Data/HPV33_P3.txt",col.names = c("plate","reads")) %>% as.data.frame()
HPV33_P3.df = HPV33_P3 %>% mutate(
  plate = gsub(x =HPV33_P3$plate, replacement = "",pattern = "_.*$") 
  %>% gsub(pattern = "-P",replacement = ".P") 
  %>% gsub(pattern = "-",replacement = "_",)
  )
HPV33_P3.df = HPV33_P3.df %>% dplyr::filter(HPV33_P3.df$plate %in% colnames(acc1_cancer_cells))
rownames(HPV33_P3.df)  <- HPV33_P3.df$plate
HPV33_P3.df$plate = NULL


HPV33_P2 = fread("./Data/HPV33_P2.txt",col.names = c("plate","reads")) %>% as.data.frame()
HPV33_P2.df = HPV33_P2 %>% mutate(
  plate = gsub(x =HPV33_P2$plate, replacement = "",pattern = "_.*$") 
  %>% gsub(pattern = "plate2-",replacement = "plate2_",)
  %>% gsub(pattern = "-",replacement = "\\.",)
  )
HPV33_P2.df = HPV33_P2.df %>% dplyr::filter(HPV33_P2.df$plate %in% colnames(acc1_cancer_cells))
rownames(HPV33_P2.df)  <- HPV33_P2.df$plate
HPV33_P2.df$plate = NULL

HPV33 = rbind(HPV33_P3.df,HPV33_P2.df)
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = HPV33,col.name = "HPV33.reads")
FeaturePlot(acc1_cancer_cells,features = "HPV33.reads",max.cutoff = 40)
```
```{r}
data = FetchData(object = acc1_cancer_cells,vars = "HPV33.reads")
print(
  data %>% 
  ggplot(aes( x=HPV33.reads)) + 
  geom_density()
)
```
```{r}
hpv33_positive = HPV33 %>% dplyr::mutate(hpv33_positive = case_when(reads >= 750 ~"strong positive",
                                                                    reads < 750 & reads > 10 ~ "positive",
                                                                    reads < 10 ~ "negative")
)



hpv33_positive$reads = NULL
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = hpv33_positive)
```

```{r}
DimPlot(object = acc1_cancer_cells,group.by  = c("hpv33_positive"),pt.size = 2)
```


# cNMF
```{r}
library(reticulate)
```

```{r}
#write expression
acc1_cancer_cells = FindVariableFeatures(object = acc1_cancer_cells,nfeatures = 2000)
vargenes = VariableFeatures(object = acc1_cancer_cells)
hmsc_expression = t(as.matrix(GetAssayData(acc1_cancer_cells,slot='data')))
hmsc_expression = 2**hmsc_expression #convert from log2(tpm+1) to tpm
hmsc_expression = hmsc_expression-1
# hmsc_expression = hmsc_expression[,!colSums(hmsc_expression==0, na.rm=TRUE)==nrow(hmsc_expression)] #delete rows that have all 0
hmsc_expression = hmsc_expression[,vargenes]
write.table(x = hmsc_expression ,file = './Data/cNMF/hmsc_expressionData_2Kvargenes.txt',sep = "\t")
```



```{python eval=F}
from cnmf import cNMF
name = 'HMSC_cNMF_2Kvargenes'
outdir = './Data/cNMF'
K_range = np.arange(3,10)
cnmf_obj = cNMF(output_dir=outdir, name=name)
counts_fn='./Data/cNMF/hmsc_expressionData_2Kvargenes.txt'
tpm_fn = counts_fn ## This is a weird case where because this dataset is not 3' end umi sequencing, we opted to use the TPM matrix as the input matrix rather than the count matrix

cnmf_obj.prepare(counts_fn=counts_fn, components=K_range, seed=14,tpm_fn=tpm_fn)
```

```{python eval=F}
cnmf_obj.factorize(worker_i=0, total_workers=1)
```

```{python eval=F}
cnmf_obj.combine()
cnmf_obj.k_selection_plot()
```
## Save object
```{python eval=F}
import pickle
f = open('./Data/cNMF/HMSC_cNMF_2Kvargenes/cnmf_obj.pckl', 'wb')
pickle.dump(cnmf_obj, f)
f.close()
```


## Load object
```{python}
from cnmf import cNMF
import pickle
f = open('./Data/cNMF/HMSC_cNMF_2Kvargenes/cnmf_obj.pckl', 'rb')
cnmf_obj = pickle.load(f)
f.close()
```


```{python}
selected_k = 4
density_threshold = 0.1
cnmf_obj.consensus(k=selected_k, density_threshold=density_threshold,show_clustering=True)
usage_norm, gep_scores, gep_tpm, topgenes = cnmf_obj.load_results(K=selected_k, density_threshold=density_threshold)
```

```{r}
# calculate usage by expression*genes coefs:
gep_scores = py$gep_scores
all_metagenes= py$usage_norm
```

## Enrichment analysis by top 200 genes of each program
```{r fig.height=8, fig.width=8, results='hide'}
plt_list = list()
for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),200) #take top top_genes_num
  res = genes_vec_enrichment(genes = top,background = rownames(gep_scores),homer = T,title = 
                    i,silent = T,return_all = T)
   
  plt_list[[i]] = res$plt
}
gridExtra::grid.arrange(grobs = plt_list)
```
```{r}
plt_list = list()
for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),10) #take top top_genes_num
  message(paste("program ",i,"top genes:"))
  print(top)

}
```

```{r fig.height=10, fig.width=10}
# Make metagene names
for (i in 1:ncol(all_metagenes)) {
  colnames(all_metagenes)[i] = "metagene." %>% paste0(i)
}

#add each metagene to metadata
for (i in 1:ncol(all_metagenes)) {
  metage_metadata = all_metagenes %>% select(i)
  acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = metage_metadata)
}

FeaturePlot(object = acc1_cancer_cells,features = colnames(all_metagenes))

```
## assignment UMAP
```{r}
larger_by = 2
message(paste("larger_by = ", larger_by))
acc1_cancer_cells = program_assignment(dataset = acc1_cancer_cells,larger_by = larger_by,program_names = colnames(all_metagenes))
selected_k =4
colors =  rainbow(selected_k)
colors = c(colors,"grey")
DimPlot(acc1_cancer_cells,group.by = "program.assignment",pt.size = 2,cols =colors)
``` 
show cell cycle program:
```{r warning=FALSE}
hallmark_name = "GO_MITOTIC_CELL_CYCLE"
genesets  =GSEABase::getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")
var_features=acc1_cancer_cells@assays$RNA@var.features
geneIds= genesets[[hallmark_name]]@geneIds
score <- apply(acc1_cancer_cells@assays$RNA@data[intersect(geneIds,var_features),],2,mean)
acc1_cancer_cells=AddMetaData(acc1_cancer_cells,score,hallmark_name)
```
```{r}
FeaturePlot(object = acc1_cancer_cells,features = hallmark_name)

```
```{r}
cc_vs_program2 = FetchData(object = acc1_cancer_cells,vars = c("metagene.2",hallmark_name))
cor(cc_vs_program2[1],cc_vs_program2[2])
```

## Comparisions


```{r}
cnv_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","hpv33_positive"))
test <- fisher.test(table(cnv_vs_hpv))
ggbarstats(
  cnv_vs_hpv, cnv.cluster, hpv33_positive,
  results.subtitle = FALSE,
  subtitle = paste0(
    "Fisher's exact test", ", p-value = ",
    ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
  )
)

```

```{r}
cnv_vs_cnmf = FetchData(object = acc1_cancer_cells,vars = c("program.assignment","cnv.cluster"))
cnv_vs_cnmf = cnv_vs_cnmf %>% dplyr::filter(program.assignment == "1" |program.assignment == "2" )
test <- fisher.test(table(cnv_vs_cnmf))
ggbarstats(
  cnv_vs_cnmf, program.assignment, cnv.cluster,
  results.subtitle = FALSE,
  subtitle = paste0(
    "Fisher's exact test", ", p-value = ",
    ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
  )
)

```

```{r}
cnmf_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("program.assignment","hpv33_positive"))
cnmf_vs_hpv = cnmf_vs_hpv %>% dplyr::filter(program.assignment == "1" |program.assignment == "2" )
test <- fisher.test(table(cnmf_vs_hpv))
ggbarstats(
  cnmf_vs_hpv, program.assignment, hpv33_positive,
  results.subtitle = FALSE,
  subtitle = paste0(
    "Fisher's exact test", ", p-value = ",
    ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
  )
)

```

```{r}
myb_vs_cnv = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","MYB"))
myb_vs_cnv $cnv.cluster = as.character(myb_vs_cnv $cnv.cluster )

ggboxplot(myb_vs_cnv, x = "cnv.cluster", y = "MYB",
          palette = "jco",
          add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("1","2")))
```



```{r}
myb_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("hpv33_positive","MYB"))
myb_vs_hpv $hpv33_positive = as.character(myb_vs_hpv $hpv33_positive )

ggboxplot(myb_vs_hpv, x = "hpv33_positive", y = "MYB",
          palette = "jco",
          add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("positive","negative"),c("strong positive", "positive"),c("strong positive", "negative")))+ stat_summary(fun.data = function(x) data.frame(y=15, label = paste("Mean=",round(mean(x),digits = 2))), geom="text") +ylab("log2(MYB)")
```

```{r}
hpvReads_vs_myb = FetchData(object = acc1_cancer_cells,vars = c("HPV33.reads","MYB"))
corr = cor(hpvReads_vs_myb$HPV33.reads,hpvReads_vs_myb$MYB)
print("correlation of MYB abd hpv33_reads:" %>% paste(corr %>% round(digits = 2)) )
```


